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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/97466

    Title: Data Mining Based Intelligent System for Voting Behavior Analysis
    Authors: Chen, Duen-Kai
    Contributors: 淡江大學資訊創新與科技學系
    Keywords: Data Mining(DM);Voting Behavior Analysis;TEDS
    Date: 2013-01
    Issue Date: 2014-03-20 14:03:22 (UTC+8)
    Publisher: Stafa-Zurich: Trans Tech Publications Ltd.
    Abstract: In this study, we report a voting behavior analysis intelligent system based on data mining technology. From previous literature, we have witnessed increasing number of studies applied information technology to facilitate voting behavior analysis. In this study, we built a likely voter identification model through the use of data mining technology, the classification algorithm used here constructs decision tree model to identify voters and non voters. This model is evaluated by its accuracy and number of attributes used to correctly identify likely voter. Our goal is to try to use just a small number of survey questions while maintaining the accuracy rates of other similar models. This model was built and tested on Taiwan’s Election and Democratization Study (TEDS) data sets. According to the experimental results, the proposed model can improve likely voter identification rate and this finding is consistent with previous studies based on American National Election Studies.
    Relation: Applied Mechanics and Materials 284-287, pp.3070-3073
    DOI: 10.4028/www.scientific.net/AMM.284-287.3070
    Appears in Collections:[資訊創新與科技學系] 期刊論文

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